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Computational models of schizophrenia and dopamine modulation in the prefrontal cortex

Nature Reviews Neuroscience volume 9, pages 696–709 (2008)Cite this article

Key Points

  • In attractor networks, a reduction in the depth of the basins of attraction of cortical attractor states destabilizes the activity at the network level owing to the constant statistical fluctuations that are caused by the stochastic spiking of neurons.

  • A decrease in the NMDA (N-methyl-D-aspartate)-receptor (NMDAR) conductances, which reduces the depth of the attractor basins, reduces the stability of short-term memory states and increases distractibility. These effects decrease the signal-to-noise ratio of the networks.

  • The cognitive symptoms of schizophrenia, such as distractibility, working-memory deficits and poor attention, could be caused by such instability of attractor states in prefrontal cortical networks.

  • Information processing in patients with schizophrenia is characterized by a diminished cortical signal-to-noise ratio during tasks that require the allocation of attention and short-term memory (as suggested by, for example, electrophysiological recordings and functional MRI).

  • In patients with schizophrenia, reduced dopamine D1 receptor activation in the prefrontal cortex can decrease the signal-to-noise ratio, at least in part by reducing NMDAR-activated synaptic currents. The underlying reason for this might be the decrease in the stability of the cortical attractor networks that is produced by a reduction in NMDAR-activated currents: this reduction decreases the depth of the basins of attraction, making short-term memory and attention unstable in the context of the spiking-related noise in cortical networks.

  • This computational approach enables us to link factors that modulate currents in synapses to the effects of this modulation on the global performance of a network — for example, to implement cognitive processes such as short-term memory and attention.

  • A reduction of NMDAR-activated synaptic conductances produces lower firing rates in neurons; in the orbitofrontal and anterior cingulate cortices this could account for the negative symptoms of schizophrenia, including reduced emotions.

  • Decreasing the GABA (g-aminobutyric acid) and the NMDA conductances produces not only switches between the attractor states, but also jumps from spontaneous activity into one of the attractor states. This might be related to the positive symptoms of schizophrenia, including delusions, paranoia and hallucinations: these symptoms could arise because the basins of attraction are shallow and there is instability in temporal lobe semantic-memory networks, leading thoughts to move too freely around the attractor energy landscape.

Abstract

Computational neuroscience models can be used to understand the diminished stability and noisy neurodynamical behaviour of prefrontal cortex networks in schizophrenia. These neurodynamical properties can be captured by simulated neural networks with randomly spiking neurons that introduce noise into the system and produce trial-by-trial variation of postsynaptic potentials. Theoretical and experimental studies have aimed to understand schizophrenia in relation to noise and signal-to-noise ratio, which are promising concepts for understanding the symptoms that characterize this heterogeneous illness. Simulations of biologically realistic neural networks show how the functioning of NMDA (N-methyl-D-aspartate), GABA (g-aminobutyric acid) and dopamine receptors is connected to the concepts of noise and variability, and to related neurophysiological findings and clinical symptoms in schizophrenia.

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Figure 1: Connectionist models.
Figure 2: Dopamine effects in the prefrontal cortex.
Figure 3: Attractor hypothesis of schizophrenia.
Figure 4: Increased cortical response variability ('noise') in patients with schizophrenia.
Figure 5: Molecular events following dopamine D1 and D2 receptor stimulation.

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Acknowledgements

This work was supported by the European Union, grant EC005-024; by the Spanish Research Project BFU2007-61710/BFI and CONSOLIDER INGENIO 2010 (G.D.); by two grants from the German Research Foundation (Deutsche Forschungsgemeinschaft): Wi1316/2-1 and Wi1316/2-2 (G.W.); by a Fellowship from the Fogarty Foundation (G.W.); by the Oxford McDonnell Centre for Cognitive Neuroscience (E.T.R.); and by the Boehringer Ingelheim Fonds (M.L.).

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Authors and Affiliations

  1. Oxford Centre for Computational Neuroscience, Oxford, UK

    Edmund T. Rolls

  2. Computational Neuroscience, Universitat Pompeu Fabra, Barcelona, 08003, Spain

    Marco Loh & Gustavo Deco

  3. Institució Catalana de Recerca i Estudis Avançats, Barcelona, 08010, Spain

    Gustavo Deco

  4. Department of Psychiatry, Laboratory of Neuropsychiatric Research, Heinrich-Heine University, Duesseldorf, 40629, Germany

    Georg Winterer

  5. Institute of Neurosciences and Biophysics, Juelich Research Centre, Juelich, 52425, Germany

    Georg Winterer

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  1. Edmund T. Rolls
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  2. Marco Loh
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  3. Gustavo Deco
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  4. Georg Winterer
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Correspondence to Edmund T. Rolls.

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Glossary

Dysexecutive syndrome

A disorder of the planning and organization of actions that is typically produced by damage to the prefrontal cortex.

Time constant

The time the system takes to reach 1/e of its initial value.

Attractor networks

Neural networks in which sets of neurons with strong interconnections have stable high-firing-rate states into which they can be attracted by memory-retrieval cues. The strong interconnections are formed during a learning period in which a set of neurons is active. An attractor net can be used to implement a short-term memory.

Long-term potentiation

(LTP). A long-term increase in synaptic strength.

Poissonian

With a Poisson distribution.

Hopfield equation

This is a measure of the stability of an attractor state that reflects the depth of the basin of attraction.

Disconnection hypothesis of schizophrenia

A hypothesis which suggests that brain regions such as the frontal and temporal lobes become relatively disconnected in schizophrenia.

Gain function

The sensitivity of a working-memory system to external stimuli in some models.

Multi-compartment Hodgkin–Huxley neurons

Models of neurons with separate biophysical parameters and modelling for the different parts of neurons, including different parts of the dendritic tree.

Basins of attraction

The shape in state space of the gradients of the low-energy, stable states into which a subset of neurons in an attractor network can be drawn.

Blood-oxygen-level-dependent (BOLD) brain response

A signal that can be extracted with fMRI and that reflects the change in the amount of deoxyhaemoglobin that is induced by changes in the activity of neurons and their synapses in a region of the brain. The signal thus reflects the activity in a local brain region.

Local field potentials

The potentials in a local brain area that reflect the activity of many neurons and their synaptic inputs.

Event-related potentials

The potentials elicited in a brain area by the activity of neurons and their synaptic inputs in response to an event or stimulus.

Gamma frequency band

The spectral frequency band of the electrical activity of the brain that is close to 40 Hz.

Phase locking

Time locking of brain oscillations to (sensory) stimuli or (motor) responses.

Broadband phase synchrony

A measure of whether the energy in different spectral frequency bands of the electrical activity of the brain is in phase.

Catechol-O-methyl-transferase (COMT) gene

The gene that encodes the COMT enzyme, which provides one of the ways in which dopamine is degraded by methylation and therefore removed from the activity at a synapse. If COMT is too active there are likely to be low levels of dopamine in the prefrontal cortex, and this might be related to the cognitive symptoms of schizophrenia.

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Rolls, E., Loh, M., Deco, G. et al. Computational models of schizophrenia and dopamine modulation in the prefrontal cortex. Nat Rev Neurosci 9, 696–709 (2008). https://doi.org/10.1038/nrn2462

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